Hate Speech Detection with Comment Embeddings

By Nemanja Djuric, Jing Zhou, R. Morris, Mihajlo Grbovic, Vladan Radosavljevic, Narayan L. Bhamidipati. The Web Conference, 2015

717 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/c9948f7213167d65db79b60381d01ea71d438f94

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Citations in the corpus

Comparative Studies of Detecting Abusive Language on Twitter (2018, methodology): “, 2012), othering language (Burnap and Williams, 2014), and hate speech (Djuric et al., 2015).”
One-step and Two-step Classification for Abusive Language Detection on Twitter (2017, background): “Djuric et al., (2015) incorporated representation word embeddings (Mikolov et al., 2013).”
Analyzing the Targets of Hate in Online Social Media (2016, background): “Existing eﬀorts are focused on studying posts of known hate groups or radical forums [1, 2, 5, 7, 10].”
Online Hate Interpretation Varies by Country, But More by Individual: A Statistical Analysis Using Crowdsourced Ratings (2018, methodology): “Different approaches have been devised by researchers to detect hateful social media comments, such as dictionary-based [8], distributional semantics [2], multi-feature [5], [21] and neural networks [22], [23].”
Context Sensitivity Estimation in Toxicity Detection (2021, background): “Toxicity detection is a popular task that has been addressed by machine learning approaches (Davidson et al., 2017; Waseem and Hovy, 2016; Djuric et al., 2015), including deep learning approaches (Park and Fung, 2017; Pavlopoulos et al., 2017b,c; Chakrabarty et al., 2019; Badjatiya et al., 2017;…”
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) (2019, background): “There have been several studies published on this sub-task such as Kwok and Wang (2013) and Djuric et al. (2015) who build a binary classi-ﬁer to distinguish between ‘clean’ comments and comments containing hate speech and profanity.”
A Unified Deep Learning Architecture for Abuse Detection (2018, methodology): “Hate speech detection [2, 12, 25, 40, 42], cyberbullying identification [6, 11, 16], and the detection of abusive [7, 10, 27] or offensive language [8, 26, 43], are some of the facets of this problem.”
Effective hate-speech detection in Twitter data using recurrent neural networks (2018, methodology): “[7], but it only performed well on a binary classification problem.”
A Survey on Hate Speech Detection using Natural Language Processing (2017, methodology): “(Nobata et al., 2016; Djuric et al., 2015; Warner and Hirschberg, 2012), YouTube (Dinakar et al., 2012), ask.fm (Van Hee et al., 2015), Formspring (Dinakar et al., 2012), Usenet (Razavi et al., 2010), Whisper 12 (Silva et al., 2016), and Xanga 13 (Chau and Xu, 2007).”
Deep Learning for Hate Speech Detection in Tweets (2017, methodology): “Most of the earlier work revolves either around manual feature extraction [6] or use representation learning methods followed by a linear classiﬁer [1, 4].”
Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection (2018, neutral): “[14].”
Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis (2016, background): “Hate speech has recently gained some interest as a research topic on the one hand – e.g. (Djuric et al., 2014; Burnap and Williams, 2014; Silva et al., 2016) – but also as a problem to deal with in politics such as the No Hate Speech Movement by the Council of Europe.”
A Survey on Automatic Detection of Hate Speech in Text (2018, background): “Some authors [25] use a paragraph2vec approach to classify language on user comments as abusive or clean and also to predict the central word in the message [25].”
Interpretable Multi-Modal Hate Speech Detection (2021, background): “…a breeding ground for abuse in the form of misuse of private user details (Yang & Srinivasan, 2014; Kontaxis et al., 2011), hate speech and cyberbullying (Djuric et al., 2015; Sood et al., 2012; Singh et al., 2017), and false or malicious information (Cheng et al., 2017; Aro, 2016; Weimann, 2010).”
Studying Generalisability across Abusive Language Detection Datasets (2019, neutral): “(Nobata et al., 2016; Djuric et al., 2015; Warner and Hirschberg, 2012), YouTube (Dinakar et al., 2011), and Wikipedia (Wulczyn et al., 2017), with annotation typically carried out on crowdsourcing platforms such as CrowdFlower (Figure Eight) and Amazon Mechanical Turk.”
Detecting Online Hate Speech Using Context Aware Models (2017, methodology): “Djuric et al. (2015) used the paragraph2vec for joint modeling of comments and words, then the generated embeddings were used as feature in a logistic regression model.”
Multilingual Sentiment Analysis and Toxicity Detection for Text Messages in Russian (2021, background): “Up to this point, the identiﬁcation of hateful and offensive statements was performed within the framework of solving the problem of detecting and classifying hate speech [7], work on which continues to this day [4], [8], [9].”
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior (2018, background): “The research community has studied hate speech (Djuric et al. 2015), cyberbullying (Hosseinmardi et al. 2015), and semi-organized on-line harassment campaigns (Hine et al. 2017), while also proposing systems to automatically detect and block abusive behavior (Ribeiro et al. 2017), (Davidson et al.…”
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods (2019, methodology): “Djuric et al. (2015) were the ﬁrst to adopt a neural approach to abuse detection.”